ChromENVEE: Chromatin ENVironment and Enhancer Expression
Coulée Manon
2022-09-08
Abstract
Citation
Introduction
# Loading package
library(ChromENVEE)Initialization of data
Generated vector to associated each chromatin state number
stateNumber to a name stateName and a color
colorValue (usable for plot generation)
stateNumber = c("U1","U2","U3","U4","U5","U6","U7","U8","U9","U10","U11","U12","U13","U14","U15","U16","U17","U18")
stateName = c("TSSA","TSSFlnk","TSSFlnkD","Tx","TxWk","EnhG","EnhG","EnhA","EnhWk","ZNF/Rpts","Het","TssBiv","EnhBiv","ReprPC","ReprPCWk","Quies","Quies","Quies")
colorValue = c("#B71C1C","#E65100","#E65100","#43A047","#1B5E20","#99FF66","#99FF66","#F5B041","#FFEB3B","#48C9B0","#B39DDB","#880E4F","#666633","#424949","#7B7D7D","#D0D3D4","#D0D3D4","#D0D3D4")Download the reference genome as a .bed file, in this
study we use mouse genome (mm10).
data(genomeFile)#> chr start end strand score gene_ENS
#> 1 chr1 3073253 3074322 + . ENSMUSG00000102693.1
#> 2 chr1 3102016 3102125 + . ENSMUSG00000064842.1
#> 3 chr1 3205901 3671498 - . ENSMUSG00000051951.5
#> 4 chr1 3252757 3253236 + . ENSMUSG00000102851.1
#> 5 chr1 3365731 3368549 - . ENSMUSG00000103377.1
#> 6 chr1 3375556 3377788 - . ENSMUSG00000104017.1Download the chromatin state file (output of ChromHMM). It’s a
.bed file which contains informations like genomic
position, information about chromatin state.
data(chromatinState)#> chr start end state name state_name
#> 1 chr10 0 3100000 U16 RS Quies
#> 2 chr10 3100000 3109200 U11 RS Het
#> 3 chr10 3109200 3110600 U12 RS TssBiv
#> 4 chr10 3110600 3111000 U14 RS ReprPC
#> 5 chr10 3111000 3111200 U13 RS EnhBiv
#> 6 chr10 3111200 3117200 U12 RS TssBivDistribution of chromatin state in the genome
We are interested to know the distribution of chromatin state in the
genome. plotChromatinState calculates the percentage of
each chromatin state in function the length of the genome used. We
obtains a data frame with the percentage of coverage for each chromatin
state and it’s possible to plot the result in .png
file.
summary_chromatin_state = plotChromatinState(chromatinState, stateName = stateName, stateNumber = stateNumber, merge = TRUE, plot = FALSE, color = colorValue, filename = "")head(summary_chromatin_state)
#> state coverage sample_name
#> TSSA TSSA 0 RS
#> TSSFlnk TSSFlnk 0 RS
#> TSSFlnkD TSSFlnkD 0 RS
#> Tx Tx 0 RS
#> TxWk TxWk 0 RS
#> EnhG EnhG 0 RS
Annotation of enhancer
We are interested to associated at each enhancer, genes regulated by
the enhancer. We focused on enhancer chromatin state (in this study, we
have 4 tpe of enhancer : bivalent enhancer (EnhBiv), genic enhancer
(EnhG), active enhancer (EnhA) and weak enhancer (EnhWk)). The file used
must to be a GRanges file
data(listTableEnhancer)#> GRanges object with 1979 ranges and 1 metadata column:
#> seqnames ranges strand | chromatin_state
#> <Rle> <IRanges> <Rle> | <character>
#> [1] chr10 9164400-9164800 * | U13
#> [2] chr10 9342200-9344000 * | U13
#> [3] chr10 10476400-10476600 * | U13
#> [4] chr10 20520200-20521000 * | U13
#> [5] chr10 20952400-20952600 * | U13
#> ... ... ... ... . ...
#> [1975] chrX 144286800-144287000 * | U13
#> [1976] chrX 155128400-155129200 * | U13
#> [1977] chrX 170010800-170013800 * | U13
#> [1978] chrY 198400-198800 * | U13
#> [1979] chrY 90786000-90788000 * | U13
#> -------
#> seqinfo: 21 sequences from an unspecified genome; no seqlengthsAnnotated enhancer binding to enhancer position
To estimated which gene is regulated by enhancer, we estimated that
genes associated enhancer, all TSS genes in interval around enhancer.
enhancerAnnotation() take few minutes to process in
function the length of your enhancer table. It’s possible to multithread
the job with the nCore parameter. For each enhancer
position, we obtains two informations, the distance between gene and
enhancer (in bp) and the gene
table_enhancer_gene = enhancerAnnotation(listTableEnhancer[[1]],genome = genomeFile, interval = 500000, nCore = 1)#> GRanges object with 1979 ranges and 6 metadata columns:
#> seqnames ranges strand | chromatin_state start_500kb end_500kb
#> <Rle> <IRanges> <Rle> | <character> <numeric> <numeric>
#> 1 chr10 9164400-9164800 * | U13 8664400 9664800
#> 1 chr10 9342200-9344000 * | U13 8842200 9844000
#> 1 chr10 10476400-10476600 * | U13 9976400 10976600
#> 1 chr10 20520200-20521000 * | U13 20020200 21021000
#> 1 chr10 20952400-20952600 * | U13 20452400 21452600
#> . ... ... ... . ... ... ...
#> 1 chrX 144286800-144287000 * | U13 143786800 144787000
#> 1 chrX 155128400-155129200 * | U13 154628400 155629200
#> 1 chrX 170010800-170013800 * | U13 169510800 170513800
#> 1 chrY 198400-198800 * | U13 -301600 698800
#> 1 chrY 90786000-90788000 * | U13 90286000 91288000
#> gene_association distance gene_list
#> <integer> <character> <character>
#> 1 19 451159;278330;340253.. ENSMUSG00000111215.1..
#> 1 21 456130;480757;457563.. ENSMUSG00000015305.6..
#> 1 20 499773;435480;392457.. ENSMUSG00000101621.2..
#> 1 16 371729;318362;311710.. ENSMUSG00000019996.1..
#> 1 21 227322;432632;326765.. ENSMUSG00000019990.1..
#> . ... ... ...
#> 1 17 459386;459456;353489.. ENSMUSG00000067276.5..
#> 1 29 492849;417142;353947.. ENSMUSG00000084367.3..
#> 1 7 325601;314901;231165.. ENSMUSG00000035299.1..
#> 1 3 7351;124495;496977 ENSMUSG00000101796.1..
#> 1 9 384752;254355;180136.. ENSMUSG00000096178.7..
#> -------
#> seqinfo: 21 sequences from an unspecified genome; no seqlengthsNumber of gene associate at the enhancer
We want to know the disribution of number of genes associated at each enhancer to adapted future filters
plotGeneAssociation(table_enhancer_gene, all = F)
Associated gene expression to enhancer
We associated the level of gene expression at each gene associated to enhancer
data(geneExpression)#> gene_ENS chr start end strand score gene_expression
#> 1 ENSMUSG00000000001.4 chr3 108107280 108146146 - . 27.7106904
#> 2 ENSMUSG00000000028.15 chr16 18780447 18811987 - . 23.5842993
#> 3 ENSMUSG00000000031.16 chr7 142575529 142578143 - . 0.9386427
#> 4 ENSMUSG00000000037.16 chrX 161117193 161258213 + . 14.4548991
#> 5 ENSMUSG00000000049.11 chr11 108343354 108414396 + . 36.6169129
#> 6 ENSMUSG00000000056.7 chr11 121237253 121255856 + . 5.2791187table_enhancer_gene_expression = enhancerExpression(table_enhancer_gene, geneExpressionTable = geneExpression)#> GRanges object with 1979 ranges and 8 metadata columns:
#> seqnames ranges strand | chromatin_state start_500kb end_500kb
#> <Rle> <IRanges> <Rle> | <character> <numeric> <numeric>
#> 1 chr10 9164400-9164800 * | U13 8664400 9664800
#> 1 chr10 9342200-9344000 * | U13 8842200 9844000
#> 1 chr10 10476400-10476600 * | U13 9976400 10976600
#> 1 chr10 20520200-20521000 * | U13 20020200 21021000
#> 1 chr10 20952400-20952600 * | U13 20452400 21452600
#> . ... ... ... . ... ... ...
#> 1 chrX 144286800-144287000 * | U13 143786800 144787000
#> 1 chrX 155128400-155129200 * | U13 154628400 155629200
#> 1 chrX 170010800-170013800 * | U13 169510800 170513800
#> 1 chrY 198400-198800 * | U13 -301600 698800
#> 1 chrY 90786000-90788000 * | U13 90286000 91288000
#> gene_association distance gene_list sample_name
#> <integer> <character> <character> <character>
#> 1 19 451159;278330;340253.. ENSMUSG00000111215.1.. RS
#> 1 21 456130;480757;457563.. ENSMUSG00000015305.6.. RS
#> 1 20 499773;435480;392457.. ENSMUSG00000101621.2.. RS
#> 1 16 371729;318362;311710.. ENSMUSG00000019996.1.. RS
#> 1 21 227322;432632;326765.. ENSMUSG00000019990.1.. RS
#> . ... ... ... ...
#> 1 17 459386;459456;353489.. ENSMUSG00000067276.5.. RS
#> 1 29 492849;417142;353947.. ENSMUSG00000084367.3.. RS
#> 1 7 325601;314901;231165.. ENSMUSG00000035299.1.. RS
#> 1 3 7351;124495;496977 ENSMUSG00000101796.1.. RS
#> 1 9 384752;254355;180136.. ENSMUSG00000096178.7.. RS
#> gene_expression
#> <character>
#> 1 NA;12.8456863815602;..
#> 1 12.8456863815602;2.0..
#> 1 NA;NA;NA;NA;NA;NA;NA..
#> 1 102.374504394998;2.0..
#> 1 0.571438637996035;3...
#> . ...
#> 1 0.255147894054393;NA..
#> 1 NA;NA;NA;12.00904498..
#> 1 0.161867853849147;NA..
#> 1 NA;NA;NA
#> 1 2.25309950916964;0.7..
#> -------
#> seqinfo: 21 sequences from an unspecified genome; no seqlengthsProfile of enhancer annotation
Distance gene-enhancer according to their expression
We generated plot to estimated the level of gene expression according to the distance between gene and enhancer
plotDistanceExpression(table_enhancer_gene_expression, color = colorValue, stateName = stateName, stateNumber = stateNumber)
Distance gene-enhancer
We generated plot to estimated the distribution of gene according to the distance between gene and enhancer
plotGeneDistance(table_enhancer_gene_expression)
Enhancer expression
We generated plot with the distribution of gene expression associated at enhancer region
plotEnhancerExpression(table_enhancer_gene_expression, scale = "log10", color = colorValue, stateName = stateName, stateNumber = stateNumber, ylab = "gene expression log10(CPM)")
Enhancer annotation comparison
It’s possible to comparated different categories of enhancer. For
that it’s necessary to use a list of GRanges file each contains data
like list_table_enhancer file.
The first step is associated gene to each enhancer using
enhancerAnnotation() on the list of enhancer. After the
gene association, we associated the gene expression using
enhancerExpression().
# list_table_enhancer_gene = lapply(listTableEnhancer, enhancerAnnotation, genome = genomeFile, interval = 500000, nCore = 1)
# list_table_enhancer_gene_expression = lapply(list_table_enhancer_gene, enhancerExpression, gene_expression_table = geneExpression)Number of gene associate at the enhancer
# plotGeneAssociation(list_table_enhancer_gene_sample, all = T)
Distance gene-enhancer in function their expression
# plotDistanceExpression(list_table_enhancer_gene_expression, color = colorValue, stateName = stateName, stateNumber = stateNumber)
Distance gene-enhancer
# plotGeneDistance(list_table_enhancer_gene_expression)
Enhancer expression
# plotEnhancerExpression(list_table_enhancer_gene_expression, scale = "log10", color = colorValue, stateName = stateName, stateNumber = stateNumber, ylab = "gene expression log10(CPM)")
Gene environment
We are interested to studying the chromatin environment of gene
stateOrderReduce = c("TSSA","TSSFlnk","TSSFlnk","Tx","Tx","EnhG","EnhG","EnhA","EnhWk","ZNF.Rpts","Het","TssBiv","EnhBiv","ReprPC","ReprPC","Quies","Quies","Quies")data(geneExpression)
data(chromatinState)Coverage of chromatin state in environment binding to TSS regions
To studying the environment of genes, we estimated as environment the
interval (interval argument) around the TSS.
geneEnvironment() may take few minutes in function the
number of genes analyzed. For each gene, we obtains informations about
the coverage of each chromatin state in the environment.
table_overlapping = geneEnvironment(geneExpression, chromatinState, stateOrder = unique(stateOrderReduce), interval = 3000)
rownames(table_overlapping) = table_overlapping$gene_ENS#> gene_ENS chr start end strand
#> ENSMUSG00000000001.4 ENSMUSG00000000001.4 chr3 108107280 108146146 -
#> ENSMUSG00000000028.15 ENSMUSG00000000028.15 chr16 18780447 18811987 -
#> ENSMUSG00000000031.16 ENSMUSG00000000031.16 chr7 142575529 142578143 -
#> ENSMUSG00000000037.16 ENSMUSG00000000037.16 chrX 161117193 161258213 +
#> ENSMUSG00000000049.11 ENSMUSG00000000049.11 chr11 108343354 108414396 +
#> ENSMUSG00000000056.7 ENSMUSG00000000056.7 chr11 121237253 121255856 +
#> score gene_expression TSS TSS_moins_3kb
#> ENSMUSG00000000001.4 . 27.7106904 108146146 108143146
#> ENSMUSG00000000028.15 . 23.5842993 18811987 18808987
#> ENSMUSG00000000031.16 . 0.9386427 142578143 142575143
#> ENSMUSG00000000037.16 . 14.4548991 161117193 161114193
#> ENSMUSG00000000049.11 . 36.6169129 108343354 108340354
#> ENSMUSG00000000056.7 . 5.2791187 121237253 121234253
#> TSS_plus_3kb TSSA TSSFlnk Tx EnhG EnhA
#> ENSMUSG00000000001.4 108149146 0.00000000 0.00000000 0 0.7423333 0
#> ENSMUSG00000000028.15 18814987 0.00000000 0.06666667 0 0.6333333 0
#> ENSMUSG00000000031.16 142581143 0.00000000 0.00000000 0 0.0000000 0
#> ENSMUSG00000000037.16 161120193 0.03333333 0.40000000 0 0.0000000 0
#> ENSMUSG00000000049.11 108346354 0.00000000 0.00000000 0 0.0000000 0
#> ENSMUSG00000000056.7 121240253 0.00000000 0.06666667 0 0.6000000 0
#> EnhWk ZNF.Rpts Het TssBiv EnhBiv ReprPC Quies
#> ENSMUSG00000000001.4 0.0000 0 0 0.2576667 0.0 0.0000 0.0000000
#> ENSMUSG00000000028.15 0.0000 0 0 0.3000000 0.0 0.0000 0.0000000
#> ENSMUSG00000000031.16 0.0000 0 0 0.0000000 0.4 0.3095 0.2905000
#> ENSMUSG00000000037.16 0.1655 0 0 0.0000000 0.0 0.0000 0.4011667
#> ENSMUSG00000000049.11 0.0000 0 0 0.3000000 0.3 0.3410 0.0590000
#> ENSMUSG00000000056.7 0.0000 0 0 0.3333333 0.0 0.0000 0.0000000Predominant state in environment binding to TSS regions
We estimated as predominent chromatin state in the environment, the
chromatin state with the higher coverage in the environment. For each
gene, we use umapr package to estimated the cluster
dimension.
result_umap = predominentState(table_overlapping, state = unique(stateOrderReduce),
header = unique(stateOrderReduce) ,neighbors = 32, metric = "euclidean", dist = 0.5)
#>
#> ==> It will be take few minutes to process#> TSSA TSSFlnk Tx EnhG EnhA EnhWk ZNF.Rpts
#> ENSMUSG00000000001.4 0.00000000 0.00000000 0 0.7423333 0 0.0000 0
#> ENSMUSG00000000028.15 0.00000000 0.06666667 0 0.6333333 0 0.0000 0
#> ENSMUSG00000000031.16 0.00000000 0.00000000 0 0.0000000 0 0.0000 0
#> ENSMUSG00000000037.16 0.03333333 0.40000000 0 0.0000000 0 0.1655 0
#> ENSMUSG00000000049.11 0.00000000 0.00000000 0 0.0000000 0 0.0000 0
#> ENSMUSG00000000056.7 0.00000000 0.06666667 0 0.6000000 0 0.0000 0
#> Het TssBiv EnhBiv ReprPC Quies UMAP1
#> ENSMUSG00000000001.4 0 0.2576667 0.0 0.0000 0.0000000 15.3444005
#> ENSMUSG00000000028.15 0 0.3000000 0.0 0.0000 0.0000000 11.2461522
#> ENSMUSG00000000031.16 0 0.0000000 0.4 0.3095 0.2905000 1.3368335
#> ENSMUSG00000000037.16 0 0.0000000 0.0 0.0000 0.4011667 0.9035514
#> ENSMUSG00000000049.11 0 0.3000000 0.3 0.3410 0.0590000 -2.2967628
#> ENSMUSG00000000056.7 0 0.3333333 0.0 0.0000 0.0000000 11.2293023
#> UMAP2 state
#> ENSMUSG00000000001.4 -9.4548692 EnhG
#> ENSMUSG00000000028.15 -0.3666459 EnhG
#> ENSMUSG00000000031.16 9.5740863 EnhBiv
#> ENSMUSG00000000037.16 3.6425823 Quies
#> ENSMUSG00000000049.11 8.4852121 ReprPC
#> ENSMUSG00000000056.7 -1.6012913 EnhGIt’s an exemple of UMAP representation to visualized the predominent
chromatin state in each gene. 
Session Information
Here is the output of sessionInfo() on the system on
which this document was compiled:
#> R version 4.1.3 (2022-03-10)
#> Platform: x86_64-conda-linux-gnu (64-bit)
#> Running under: Ubuntu 18.04.6 LTS
#>
#> Matrix products: default
#> BLAS/LAPACK: /home/mcoulee/anaconda3/envs/R_package_3/lib/libopenblasp-r0.3.20.so
#>
#> locale:
#> [1] LC_CTYPE=fr_FR.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=fr_FR.UTF-8
#> [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=fr_FR.UTF-8
#> [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] ChromENVEE_1.1.7
#>
#> loaded via a namespace (and not attached):
#> [1] Rcpp_1.0.9 lattice_0.20-45 png_0.1-7
#> [4] prettyunits_1.1.1 ps_1.7.1 assertthat_0.2.1
#> [7] rprojroot_2.0.3 digest_0.6.29 utf8_1.2.2
#> [10] RSpectra_0.16-1 R6_2.5.1 GenomeInfoDb_1.30.0
#> [13] stats4_4.1.3 evaluate_0.15 highr_0.9
#> [16] ggplot2_3.3.6 pillar_1.8.0 zlibbioc_1.40.0
#> [19] rlang_1.0.4 callr_3.7.1 jquerylib_0.1.4
#> [22] S4Vectors_0.32.3 Matrix_1.4-1 reticulate_1.25
#> [25] rmarkdown_2.14 labeling_0.4.2 splines_4.1.3
#> [28] devtools_2.4.3 stringr_1.4.0 umap_0.2.9.0
#> [31] RCurl_1.98-1.7 munsell_0.5.0 compiler_4.1.3
#> [34] xfun_0.31 askpass_1.1 pkgconfig_2.0.3
#> [37] BiocGenerics_0.40.0 pkgbuild_1.3.1 mgcv_1.8-40
#> [40] htmltools_0.5.3 openssl_2.0.2 tidyselect_1.1.2
#> [43] tibble_3.1.7 GenomeInfoDbData_1.2.7 IRanges_2.28.0
#> [46] fansi_1.0.3 crayon_1.5.1 dplyr_1.0.9
#> [49] withr_2.5.0 bitops_1.0-7 grid_4.1.3
#> [52] nlme_3.1-158 jsonlite_1.8.0 gtable_0.3.0
#> [55] lifecycle_1.0.1 DBI_1.1.3 magrittr_2.0.3
#> [58] scales_1.2.0 cli_3.3.0 stringi_1.7.8
#> [61] cachem_1.0.6 farver_2.1.1 XVector_0.34.0
#> [64] fs_1.5.2 remotes_2.4.2 bslib_0.4.0
#> [67] ellipsis_0.3.2 vctrs_0.4.1 generics_0.1.3
#> [70] tools_4.1.3 glue_1.6.2 purrr_0.3.4
#> [73] processx_3.7.0 pkgload_1.3.0 parallel_4.1.3
#> [76] fastmap_1.1.0 yaml_2.3.5 colorspace_2.0-3
#> [79] GenomicRanges_1.46.1 sessioninfo_1.2.2 memoise_2.0.1
#> [82] knitr_1.39 sass_0.4.2 usethis_2.1.6References
Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nature Methods, 9:215-216, 2012
Papier scientifique associé
McInnes, Leland, and John Healy. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.” arXiv:1802.03426.
Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan M, Carey V (2013). “Software for Computing and Annotating Genomic Ranges.” PLoS Computational Biology, 9. doi: 10.1371/journal.pcbi.1003118, http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1003118.